1. Field of the Invention
The present invention relates to an apparatus for relocating spatial information for use in, for example, a pattern information processing apparatus, a data processor, a spatial filter apparatus, a parallel computer, and a systolic array processor.
2. Description of the Prior Art
It is well known that the visual function of animals is an important function which may relate to the preservation of the species, so that it has become a highly developed organ through the process of evolution.
The investigation on artificial simulation of a pattern recognition function, represented by the visual sense, was begun in the 1950's on the basis of analog techniques. With the subsequent development of digital techniques, the investigation on pattern recognition systems by computer processing is now in progress in a variety of fields.
Perceptron by Rosenblatt is well known as an example of a pioneer investigation of this kind, the structure of which is schematically shown in FIG. 1.
The perceptron is a kind of hierarchical neural network comprising three layers S, A and R. More specifically, the perceptron is formed by the S-layer (sensing layer) corresponding to a receptor, the A-layer (association layer) corresponding to a bipolar cell, the R-layer (reaction layer) corresponding to a ganglion, and random connections corresponding to groups of synapses for coupling the S-, A- and R-layers, where a series of input patterns and a multiplicity of expected outputs are provided. While these expected outputs are repeatedly proposed, a parameter for the R-layer is controlled to gradually approach an actual output and thereby improve the ratio of generating a true answer.
Although the perceptron was an extremely epoch-making system as a learning machine and exhibited considerable performance for problems within a limited range, the results of subsequent logical analysis has found that there are a large number of problems which cannot be solved by this system. For this reason, the perceptron is now hardly studied, however, investigation on improvement of this kind of system is still in progress on the basis of the perceptron in a variety of industrial fields.
On the other hand, since a large volume of processing is required for acquired data, it is necessary to make countermeasures for complicated processing, for example, high speed processing by using a plurality of processors. It is said that processing by the conventional Neumann type computer has already reached its limit.
For this reason, the computer architecture is being radically reviewed for such an application in a variety of fields. One of these approaches is a systolic array processor.
The systolic array processor is a system in which a multiplicity of single-function processors are plainly located, and respective adjacent processors are coupled through data lines with each other. Then, data parallelly supplied from the processor located at the end are processed by the individual processors to perform composite pipe-line processing. Thus, the systolic array processor was developed for the purpose of deriving parallel outputs at a high speed. This system allows a special processor for performing matrix calculation and so on requiring an immense time for processing to be implemented by integrated circuits.
The sequential type digital processing, although advantageously capable of accessing arbitrary data in a given data range and processing the data correctly, typically incurs a rapid and large increase in cost with respect to both hardware and software if an immense amount of data such as visual information is to be processed at a high speed.
According to the results of an investigation on the visual sense of animals, it is regarded that remarkable information processing has already been performed at the retina which serves as receptor, and the visual sense center in the brain receives summarized results transmitted thereto which may be called "essence." From this point of view, there have been made many attempts on simulating the retina by analog electronic circuits, and many results have already been reported.
It is said that not only the retina but also the neural organization in general is constituted by complicated synapses couplings between a plurality of neural cells. If such an organization is actually simulated by electronic circuits, the realization of the synaptic couplings suffers from considerable difficulties, and a large amount of spatial connections are required. For example, the perceptron actually simulates synaptic coupling in this way, however, connections in hardware are not easily changed, thereby presenting a low freedom.